{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "step4: 画出采集信号的频谱图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.fftpack import fft,ifft\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pylab import mpl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mpl.rcParams['font.sans-serif'] = ['SimHei']   #显示中文\n",
    "mpl.rcParams['axes.unicode_minus']=False       #显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.linspace(0,1,1400)      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=7*np.sin(2*np.pi*200*x) + 5*np.sin(2*np.pi*400*x)+3*np.sin(2*np.pi*600*x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 21407 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 22987 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 27874 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 24418 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 21407 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 22987 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 27874 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 24418 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 37096 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 65288 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 21069 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 32452 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26679 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 65289 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 37096 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 20998 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 65288 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 21069 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 32452 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26679 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 26412 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/opt/conda/lib/python3.7/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 65289 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(x,y)   \n",
    "plt.title('原始波形')\n",
    " \n",
    "plt.figure()\n",
    "plt.plot(x[0:50],y[0:50])   \n",
    "plt.title('原始部分波形（前50组样本）')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
